Researchers have developed new algorithms for profit maximization in bilateral trade scenarios, focusing on a broker's regret minimization. The study introduces a learning algorithm with a $\tilde O(T^{\frac 23})$ regret rate for adversarial settings, which is nearly optimal. For independent distributions satisfying the monotone-hazard-rate property, a different algorithm achieves nearly tight $\tilde{O}(\sqrt{T})$ regret. The paper also demonstrates a theoretical separation between monotone-hazard-rate and regular distributions, showing a minimax regret rate of $\tilde \Theta(T^{\frac 35})$ for the latter. AI
IMPACT Introduces advanced learning algorithms that could optimize trading strategies in complex economic environments.
RANK_REASON Academic paper detailing novel algorithms and theoretical bounds for a specific problem in machine learning and economics. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- bilateral trade
- broker
- cs.LG
- Hugging Face
- Minimax
- monotone-hazard-rate
- stochastic convex bandit
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